{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f947babd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitting(A,b):\n",
    "    return np.linalg.inv(A.T @ A) @ (A.T@ b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c5df20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calA(X):\n",
    "    A = np.zeros((X.size,3))\n",
    "    A[:,0] = 1\n",
    "    A[:,1] = X\n",
    "    A[:,2] = X ** 2\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a578e26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calb(Y):\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c24c5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3590063",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0,10,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c69d5ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,\n",
       "        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,\n",
       "        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,\n",
       "        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,\n",
       "        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,\n",
       "        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,\n",
       "        7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,\n",
       "        8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,\n",
       "        9.9, 10. ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6fcd79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#R = 1\n",
    "#C = 1\n",
    "#i = 5 / R * np.exp(- t / (R * C)) + (np.random.rand(t.size) - 0.5) * 0.0001\n",
    "a1 = 1\n",
    "a2 = 1\n",
    "a3 = -2\n",
    "i = a1 + a2 *t + a3 *(t ** 2) + (np.random.rand(t.size)-0.5)* 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a02ff116",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Y = np.log(i)\n",
    "b = calb(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5abc4fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = calA(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a3b04309",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = fitting(A,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "15bb4510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.99614156,  1.0019384 , -2.00018999])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c590b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_fitting = 5 / np.exp(result[0])\n",
    "C_fitting = -1 / result[1] / R_fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a948cdf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8465081188396164"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f65d1087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0015504669426596"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9cd28b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#i_fitting = 5 / R_fitting * np.exp(- t / (R_fitting * C_fitting))\n",
    "i_fitting = result [0] + result[1] + result[2] * (t ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09e915d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21a121978b0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SS8J22yV/tu2EJGVdSLfM0KGHQ3gMWC7FrZoY4z1NY2qASmCPGGMMIfQE+sQYp4UQNgLuBtaNMX6T4vtXA9UAgwYN2qi+vn6R5yoJ+P57OOkkuOYa+L//g1tugTXWoLa2lpqaGiZNmsSgQYP47rvvmDZtWqvHy8vLqaury/68JanAhBBejjFWtrremeDVgR86HDgc2DbG2JBmzFPASTHG8W19r8rKyjh+fJtDJHXUPfckfb9mzYKrr07qwZopKytLWfsVQqCxsTFbs5SkgpUueGVyV+OOwCnAbs1DVwhhYAihW9PXqwJrAB9mah6SUhg2DCZMgMGD4YADYPhw+Pbb+bfTtZ2w7kuSOieTNV5XAksCjy7QNuKnwOshhNeA24HDY4ypuzpKypyVV4YnnoCzzoIbb4SNNoJXXgGs+5KkTMnoUmNXcqlRyqBnnoGqKvjvf+HSS+GII6i96SbrviRpEWV9qVFSAfnpT+HVV5NzH486CvbZh6pddvG4IUnqYgYvSYlllkl6fV1wAdx5Z4ulR/C4IUnqCgYvSf9TVpb0+Hr6aZgxAzbbDK66yuOGJKmLGLwktfaTnyS7HrfdNll63H9/qn7+c48bkqROMnhJSm2ZZeD+++Gcc+Dmm2HjjanaaCOPG5KkTjB4SUqvrAxOOw0eeSTZ8fjjH8Ntt82/bdsJSVo4Bi9J7RsyJNn1uP76sM8+cPzxMHs2VVVVrZYf+/bty6xZs1o8bt2XJCXs4yWp42bNgpNPhiuuSFpQ3HorLNfyuFaPG5Ik+3hJ6gqLLQaXXw61tfDSS0nLieeeazHE44YkKT2Dl6SF98tfwvPPw+KLw1ZbwTXXQNNbLuu+JCk9g5ekRbPBBjB+fFL/dcQRcOihMHOmdV+S1AZrvCR1TmNjctD2OefAJpskXe9XWKHFEOu+JJUaa7wkZUZZGZx9NtxxB0ycCJWVyTJkM9Z9SVLC4CWpa+yxRxK4evVK6r7GjJl/y7ovSUoYvCR1nfXWS3Y7brUVHHIIHHMMzJlj3ZckNbHGS1LXmzMHTjkFLrkkKb6/7TZYeukWQ6z7klTMrPGSlD3du8PFF8MNN8AzzyRF9++802KIdV+SSpHBS1LmHHQQPPEEfP01bLopPPTQ/FvWfUkqRQYvSZn1k5/Aiy9CRQXsvDNcdhnEaN2XpJJkjZek7PjuOzjwQLjrLjjsMBg1Cnr0aDHEui9JxcIaL0m51acP3H57UnR/7bXJ26+vvmoxJF3dV7rrklRoDF6SsqesDM4/P+nx9eSTsPnm8OGH82+nqvvq3bs3Q4cOteBeUlEweEnKvl/9Ch59FD79NNnxOG4cQMq6r+HDhzN27FgL7iUVBWu8JOXOv/8Nu+wC9fUwdizsu2+rIRUVFdTX17e6Xl5eTl1dXRYmKUkLzxovSflnzTWTY4Y23RT22w/OOw8W+I/BSZMmpXw03XVJymcGL0m51b8/PPII/PKXcOqpUF0Ns2fPv22jVUnFxOAlKfd69oQbb4TTToPrr0+WH7/5BrDRqqTiYvCSlB9CgHPOgT//Oel2v+WWMHmyjVYlFRWL6yXln0cfhT33hKWWgn/8A9Zbr8VtG61KyncW10sqHNttlxyuPXcubLEFPP10i9ttNVqtra219ktS3jJ4ScpPG26Y7HhcYQXYfnu49db5t9pqtFpdXW3tl6S8ZfCSlL8GDUqaq26ySdLj65JLgNSNVkePHs2DDz5IQ0NDi29h7ZekfGKNl6T8N2MGHHBActbjiSfChRcmxw8twNovSfnCGi9JhWvxxZOlxmOOgYsvhuHDW/T6msdDtiXlO4OXpMJQVgaXXw4jRyY9v3bdFb77rsUQD9mWlO+653oCktRhISTd7X/wAzjsMNh2W3jgAVhmGSCp/QKoqalh0qRJDBo0iKFDhzJ27Nj5tV/zCu6bj5ekbLHGS1Jhuvde2GefpAD/kUegvDzlMA/ZlpQL1nhJKi677ZY0Wv3ss6TX19tvpxzmIduS8onBS1Lh2mKLpNHq7NnJEUMvvdRqiAX3kvKJwUtSYdtgg6TXV9++sM028PjjLW5bcC8pnxi8JBW+1VaDZ5+FigoYOhTuumv+rVTNVocPH87YsWPtcC8p6yyul1Q8vvgCdtkFXngB/vxnOOiglMMsuJeUaRbXSyp+/fsnBffbbgu/+hWMGpVymAX3knLF4CWpuCyxBNx3H+y+Oxx7LJx7LizwZj9dYX3//v2t+5KUUQYvScWnZ0+47TbYf3+oqYHf/a5F+EpVcN+jRw++/fZb674kZZTBS1Jx6t4dxo6FI45IDtU+8khoOig7VcF93759mTVrVotv0dDQQE1NTS5mL6lIWVwvqbjFCCNGwAUXwAEHwJgxSShbQFlZGan+eRhCoLEpsElSR6UrrvesRknFLQQ4/3zo0wdOPx1mzIDaWujRo8WwQYMGpdzpaKNVSV3JpUZJpeG00+CPf4S//x323htmzmxx20arkrLBN16SSseJJ8Lii8PRR8OwYXDnndAUtqqqqgCoqalh0qRJDBo0iKFDhzJ27FgaGhoA5hfcNx8vSQvDGi9JpWfMGDj0UNhqq6T1RJ8+KYfZaFXSorKBqiTNc/DBcOON8M9/wo47wrffphxmo1VJXS1jwSuEcFYIYUoIYULTZ2izeyNCCO+HEN4NIeyQqTlIUlq//CXccktyvNAOO8DXX7cakq6w3oJ7SYsq02+8Lo0xbtj0eRAghLAOsC+wLrAjcFUIoVuG5yFJre21V9Jo9aWXYPvt4auvWty24F5SV8vFUuMw4JYY48wY43+A94GNczAPSUqOFrrjDnj1VRgyJDlou0mqRqvDhw9n7NixdriXtEgyHbyODiG8HkIYE0JYuunaisBHzcZMbromSbmx225w113wxhvJAdvTps2/VVVVRV1dHY2NjdTV1fHggw/O3+U4jx3uJXVUp4JXCOGxEMLEFJ9hwNXAasCGwCfAxfMeS/GtUm6tDCFUhxDGhxDGT506tTNTlaS27bwz3HMPvP12q/DVnAX3kjqjU8Erxjgkxrheis89McbPYoxzY4yNwHX8bzlxMrBys2+zEvBxmu8/OsZYGWOsHDhwYGemKknt23FHuPdeeOedtOHLgntJnZHJXY3LN/vj7sDEpq/vBfYNIfQMIawCrAG8mKl5SNJC2X77/4WvIUNahS8L7iV1RiY7118YQtiQZBmxDjgMIMb4ZgjhNuAtYA5wVIxxbgbnIUkLZ/vtk2XHYcNgu+3gscegf3/ADveSOsfO9ZKUzkMPwc9/Duus0yJ8LcgO95IWZOd6SVpYO+4Id98Nb72VvPlaoM/XPBbcS+oog5cktWXHHZPDtN94I+lw/803rYZYcC+powxektSeoUPh73+HV16BnXaC775rcduCe0kdZfCSpI4YNux/ZzvuvDN8//38W3a4l9RRFtdL0sK45RaoqoKtt4b774devVIOs+BeKm0W10tSV9h3X/jLX+DJJ5NzHmfOTDnMgntJqRi8JGlhHXAAXH89PPxwEsRmz241xIJ7SakYvCRpURx8MIwalbSbOPBAmNuyD7QF95JSyWTnekkqbkcfDQ0NcMopSa3X9ddDWfLfs3a4l5SKxfWS1FlnnQW//z0cdVTyFiyElMMsuJdKR7riet94SVJnnXlm8ubroougd2+44IKU4cuCe0kGL0nqrBCSsDUvfC21FNTUtBo2aNCglG+8LLiXSofF9ZLUFUKAK65Idjyedlqy5LiAdAX3I0eOzNYsJeWYwUuSukpZGYwZAz//ORx7LIwd2+J2qg73o0ePBnCno1QiDF6S1JW6d0+62w8ZkrScuOOOFrerqqqoq6ujsbFxfkF9dXW1RwtJJcJdjZKUCd9/D9ttB+PHw333wQ47pBzmTkepOHlkkCRl0xJLwIMPwjrrwB57wL/+lXKYOx2l0mLwkqRM6dcvOVZohRVg553hjTdaDfFoIam0GLwkKZOWXRYefTTp77XDDvDhhy1ue7SQVFoMXpKUaRUV8MgjMHMmbL89fPrp/FupdjoOHz6csWPHWnAvFSGL6yUpW154AbbdFlZfHZ56KlmKTMGCe6nwWVwvSbm2ySZw113w1luw664wfXrKYRbcS8XL4CVJ2bTddlBbC+PGwT77wJw5rYZYcC8VL4OXJGXb3nvDlVcm/b0OOwwWKPnwaCGpeBm8JCkXjjwSzjgjOWJogQO1PVpIKl4GL0nKlbPOSt54nXceXH55i1seLSQVJ3c1SlIuzZ0Lv/gF3Hkn3HQT7LdfymHudJQKi7saJSkfdeuWFNtvvTUMHw6PPZZymDsdpeJg8JKkXFt8cbj7blh77eRcxwkTWg1xp6NUHAxekpQPlloK/vGPpKnqTjvBAsuHHi0kFQeDlyTlixVXhIceSo4W2nFHmDZt/i2PFpKKg8X1kpRvnn0WhgyBwYPh8ceTA7ZTsOBeyl8W10tSodhii2SH4wsvwL77puxuDxbcS4XI4CVJ+WiPPWDUqKS7/bHHtupuDxbcS4XI4CVJ+eqoo+Dkk+Hqq+Gii1rd9mghqfAYvCQpn51/frLceMopcPPNLW6lO1qoqqqK2tpadztKecjieknKdzNnwvbbw/PPwyOPwFZbtTm8traW6upqGhoa5l/r3bv3/FAmKfPSFdcbvCSpEHzxBfzkJ/DppzBuHKyzTtqh7naUcs9djZJUyPr3TxqsLr540mD1k0/SDnW3o5S/DF6SVCgqKuD+++Hzz2G33eD771MOc7ejlL8MXpJUSDbaCG65BV55BaqqYO7cVkPc7SjlL4OXJBWaXXeFyy6De+6Bk05qdTvdbkfAnY5Sjhm8JKkQHXMMHHdcEsCuvLLV7aqqKurq6mhsbJxfUF9dXe25jlKOuatRkgrV3LlJh/v770/efu2yS9qh7nSUsstdjZJUbLp1S850HDw4abL66qtph7rTUcoPBi9JKmRLLJGc59i/f1L79fHHKYe501HKDwYvSSp0yy+fhK+vv07CV4o2E+50lPKDwUuSisGPfpS0mZgwAfbfHxobW9x2p6OUHwxeklQsdt4ZLrkE7r4bRoxoddudjlLuuatRkopJjHDUUXD11XD99XDIIWmHutNRypx0uxq752IykqQMCQGuuAI++AAOPxxWXRV+9rOUQ93pKGWfS42SVGy6d4fbboM11oC99oL33085zJ2OUvYZvCSpGC21VLLTMYRkp+NXX7Ua4k5HKfsyFrxCCLeGECY0fepCCBOarleEEKY3u3dNpuYgSSVttdXgjjuSN1777ANz5rS47U5HKfsyFrxijPvEGDeMMW4I3AHc2ez2B/PuxRgPz9QcJKnkbbUVXHMNPPIInHBCq9vudJSyK+NLjSGEAPwCuDnTP0uSlMIhhySha9SoJIS1oaamhoaGhhbXGhoaqKmpyeQMpZKRjRqvLYHPYozvNbu2Sgjh1RDC0yGELbMwB0kqbRdeCEOHwtFHwxNPpB3mTkcpszoVvEIIj4UQJqb4DGs2bD9avu36BBgUYxwMnADcFELom+b7V4cQxocQxk+dOrUzU5Wk0tatG9x8M6y1Fuy9d9JuIgV3OkqZ1angFWMcEmNcL8XnHoAQQndgD+DWZs/MjDFOa/r6ZeADYM003390jLEyxlg5cODAzkxVktS3L9x7b9Jkdbfd4JtvWg1xp6OUWZleahwCvBNjnDzvQghhYAihW9PXqwJrAB9meB6SJEh2Ov797/Duu57pKOVApoPXvrQuqv8p8HoI4TXgduDwGOMXGZ6HJGmebbeFyy5L+nyddlqr2+50lDLHsxolqRTFCIcdBtddBzfdBPvtl3aoZzpKCy/dWY12rpekUhQCXHklbLklHHwwtPEftu50lLqOwUuSStViiyWd7X/wA9h9d/jss5TD3OkodR2DlySVsoED4e67Ydo02HNPmDWr1RB3Okpdx+AlSaVu8GD4859h3Dg49thWt93pKHUdg5ckKSmu/+1v4dprk88C3OkodQ13NUqSEnPnwi67wOOPw5NPwk9+knaoOx2ltrmrUZLUtm7dktYS5eVJvdfkyWmHutNRWjQGL0nS/yy9NNxzD3z/PeyxB8yYkXKYOx2lRWPwkiS1tM468Ne/wksvwVFHJc1WF+BOR2nRGLwkSa3tvntynNCYMWmL7VPtdKyqqsrBZKXCYfCSJKV21lmw005Ji4l//avV7QV3OlZVVVFbW2uLCakNBi9JUmrdukFtLQwalBTbf/xxm8Nra2ttMSG1w+AlSUpv6aWTzvbffgt77ZWys/08NTU1NDQ0tLjW0NBATU1NhicpFQ6DlySpbeutBzfcAM89B8cfn3aYLSak9hm8JEnt23vvpLP91VfD2LEph9hiQmqfwUuS1DEjR8LPfgaHHw6vvpriti0mpPYYvCRJHdO9O9xyCyyzTFJs/8UXLW57mLbUPoOXJKnjfvADuP12mDIFqqqgsbHFbQ/Tltpm8JIkLZxNNoErroCHHoLf/77Noe50lFoyeEmSFl51NRx0EJx9Ntx/f9ph7nSUWjJ4SZIWXghw1VUweDAccAB8+GHKYe50lFoyeEmSFk2vXkm9FyTNVWfMaDXEnY5SSwYvSdKiW3VV+NvfkvYSxxzT6raHaUstGbwkSZ2zyy5w6qlw/fVJh/sFeJi29D8GL0lS5519NmyzDRx5JEyY0OZQD9NWKQsxxlzPoUMqKyvj+PHjcz0NSVI6//1vUmzfqxeMHw/9+qUcVlFRQX19favr5eXl83t/SYUuhPByjLFyweu+8ZIkdY0f/AD+/neor09aTaT5D3tbTKiUGbwkSV1n883hoovgnnvgkktSDrHFhEqZwUuS1LWOOw722ANOOQXGjWt12xYTKmUGL0lS1woBxoyBigrYZx+YOrXFbQ/TVikzeEmSut5SSyXNVT//HPbfH+bObXHbw7RVqgxekqTM2HBDGDUKHnkE2llG9DBtlQqDlyQpcw49NHnjddZZ8NhjaYe501GlwuAlScqcEOCaa+CHP4SqKvjkk5TD3OmoUmHwkiRl1hJLJP29vvsOfvnLVvVe4E5HlQ6DlyQp89ZZB666Cp56KjleaAEepq1SYfCSJGXH8OFJR/tzzklZ7+Vh2ioFBi9JUvZceWW79V7zeJi2ipHBS5KUPUssAbfdBt9+m4SvFPVe89hiQsXI4CVJyq51103qvZ58MmW91zy2mFAxMnhJkrLvoIOSmq9zzkkCWAq2mFAxMnhJknLjyithzTWTJcf//rfVbVtMqBgZvCRJudGnT1Lv9cUXyduvxsYWt20xoWJk8JIk5c4GG8Bll8FDD8HFF7e6bYsJFRuDlyQptw47DPbaC049FZ5/vs2htphQoQsxxlzPoUMqKyvj+PHjcz0NSVImfPUVDB4MMcKrr8LSS6ccVlFRQX19favr5eXl1NXVZXaO0kIIIbwcY6xc8LpvvCRJudevH9x6K0yZAocemgSwFGwxoUJn8JIk5YeNN4bzzoM774TRo1MOscWECp3BS5KUP044AXbYAY4/Ht58s9VtW0yo0Bm8JEn5o6wMxo6Fvn1hn31g+vQWt20xoUJn8JIk5Zdll4W//S1543Xiia1u22JChczgJUnKP9tvDyefDFdfndR8tcEWEyoktpOQJOWnWbNgiy3gvffgtdcgTQG9LSaUjzLSTiKEsHcI4c0QQmMIoXKBeyNCCO+HEN4NIezQ7PpGIYQ3mu5dEUIInZmDJKlILbYY3HwzzJ2bnOc4Z07KYbaYUCHp7FLjRGAP4JnmF0MI6wD7AusCOwJXhRC6Nd2+GqgG1mj67NjJOUiSitVqqyXLjc8+C+eem3KILSZUSDoVvGKMb8cY301xaxhwS4xxZozxP8D7wMYhhOWBvjHG52KyxvlX4OedmYMkqchVVSWfs8+Gf/2r1W1bTKiQZKq4fkXgo2Z/ntx0bcWmrxe8LklSen/6U1LjVVUFX3/d4pYtJlRI2g1eIYTHQggTU3yGtfVYimuxjevpfnZ1CGF8CGH81KlT25uqJKlYLbUU1NbCRx/BkUe2um2LCRWK7u0NiDEOWYTvOxlYudmfVwI+brq+Uorr6X72aGA0JLsaF2EekqRisdlmcOaZcMYZsNNOsP/+aYfOazHR0NAAML/FBOCbMOVUppYa7wX2DSH0DCGsQlJE/2KM8RPg2xDCpk27GQ8E7snQHCRJxebUU2HLLZO3Xh98kHZYTU3N/NA1T0NDAzU1NZmeodSmzraT2D2EMBnYDHgghPAwQIzxTeA24C3gIeCoGOPcpseOAK4nKbj/APhHZ+YgSSoh3brBjTcmRwtVVcHs2SmH2WJC+aqzuxrvijGuFGPsGWNcNsa4Q7N7I2OMq8UY14ox/qPZ9fExxvWa7h0dC6WDqyQpPwwaBKNHwwsvwDnnpBliiwnlJ48MkiQVnl/8Ag48EEaOhHHjWt22xYTylcFLklSYRo2C8vKkyP6bb1rcssWE8pXBS5JUmPr2Teq9Jk2CY45pdTtViwnANhPKKYOXJKlwbb45nHYa/PWvcNtt7Q6f12aivr6eGOP8NhOGL2VLKJTa9srKyjh+/PhcT0OSlG/mzIEttoB334XXX4eVV047tKKigvr6+lbXy8vLqaury+AkVWpCCC/HGCsXvO4bL0lSYevePVlynD07KbhvbEw71DYTyjWDlySp8K2+OlxxBTz1FFxySdphtplQrhm8JEnF4Ve/gt13h5qaZMkxBdtMKNcMXpKk4hBC0lh16aWTrvYzZrQaYpsJ5ZrBS5JUPJZZBsaMgYkTkzdfKaRqM2GLCWWLwUuSVFyGDoUjjkhqvZ54ot3htphQNtlOQpJUfBoaYPDg5K+vv54sP6Zhiwllgu0kJEmlo3fvpMXEp5/CUUe1OdQWE8omg5ckqTj9+Mdwxhlw881wyy1ph9liQtlk8JIkFa8RI2CTTeDII2HKlJRDbDGhbDJ4SZKKV/fuyTmOM2bAIYdAirpmW0womwxekqTituaacNFF8PDDcO21KYfYYkLZYvCSJBW/I4+E7baDE0+E995rd7gtJpQptpOQJJWGyZNh/fXhhz+EZ55JliHTsMWEOst2EpKk0rbSSvCnP8FzzyVLj22wxYQyxeAlSSod++0Hv/gFnHkmTJiQdpgtJpQpBi9JUukIAa66CgYMgOHDYebMlMNsMaFMMXhJkkrLgAFw3XXJUUJnn51yiC0mlCkW10uSStMhh8Bf/gL/+lfSZFXqQhbXS5LU3KWXJgX3Bx6YHKbdAfb2UmcZvCRJpalvXxgzBv79b6ipaXe4vb3UFVxqlCSVtmOOgSuvhCefhK23TjvM3l5aGOmWGg1ekqTS9v33sOGGMGdOUnC/5JIph5WVlZHq35khBBobGzM8SRUaa7wkSUpliSVg7Fior4eTT047zN5e6goGL0mSNt88Ocfx2mvh0UdTDrG3l7qCwUuSJEh6eq29dtJm4ptvWt22t5e6gsFLkiSAXr2Svl5TpiRvv1Koqqqirq6OxsZG6urqqKqqssWEForBS5KkeTbZJKnzuv56eOihdofbYkILy12NkiQ1N2MGbLQRfP01TJwI/fqlHWqLCaXjrkZJkjpi8cWTJcdPP4Xf/KbNoZMmTVqo65LBS5KkBf34x/C73yUB7P770w6zxYQWlsFLkqRUTj8d1lsPDjsMvvoq5RBbTGhhGbwkSUqlZ0+44Qb47DM44YSUQ2wxoYVlcb0kSW059VQ47zx48EHYaadcz0YFwuJ6SZIWxZlnwjrrQHV1stOxA+ztpXQMXpIktWXekuPHH8NJJ7U73N5eaovBS5Kk9my8cRK6rr8eHnmkzaE1NTU0NDS0uNbQ0EBNTU0mZ6gCYY2XJEkdMWMGbLghTJ+eNFZdcsmUw8rKykj179YQAo2NjRmepPKFNV6SJHXG4osnS44ffQS//W3aYfb2UlsMXpIkddRmm8Hxx8M118BTT6UcYm8vtcXgJUnSwvjDH2DVVeHQQ2GBWi6wt5faZo2XJEkL68knYZtt4MQT4Y9/zPVslIes8ZIkqav87GfJUUKXXgovvNChR+ztJTB4SZK0aC68EFZYAQ4+GGbObHOovb00j8FLkqRF0bcvXHstvPVWUvfVBnt7aR6DlyRJi2roUDjgADj/fJgwIe2wSZMmLdR1FS+DlyRJnXHZZTBgABxyCMyZk3KIvb00j8FLkqTO6N8fRo2CV15Jiu1TsLeX5ulU8Aoh7B1CeDOE0BhCqGx2fbsQwsshhDea/rpNs3tPhRDeDSFMaPr8oDNzkCQp5/baC4YNgzPOgPffb3Xb3l6ap7NvvCYCewDPLHD9c2DXGOP6wHDgbwvcr4oxbtj0+W8n5yBJUm6FAFddBT17wq9/DSl6ZFZVVVFXV0djYyN1dXVUVVXZYqIEdSp4xRjfjjG+m+L6qzHGj5v++CaweAihZ2d+liRJeW2FFeCii5KjhK6/vt3htpgoTdmo8doTeDXG2LzJyQ1Ny4ynhxBCFuYgSVLmHXoobL01nHwyfPxxm0NtMVGa2g1eIYTHQggTU3yGdeDZdYELgMOaXa5qWoLcsulzQBvPV4cQxocQxk+dOrX9vxtJknIpBLjuuqSh6lFHpVxynMcWE6Wp3eAVYxwSY1wvxeeetp4LIawE3AUcGGP8oNn3m9L012+Bm4CN2/jZo2OMlTHGyoEDB3b070mSpNxZfXU4+2y4+264/fa0w2wxUZoystQYQugHPACMiDGOa3a9ewhhmaavewC7kBToS5JUPH7zG9hoIzjmGPjyy5RDbDFRmjrbTmL3EMJkYDPggRDCw023jgZWB05foG1ET+DhEMLrwARgCnBdZ+YgSVLe6d49WXL8/POk3isFW0yUphDbWH/OJ5WVlXH8+PG5noYkSR13yinJYdpPPpkU3atkhBBejjFWLnjdzvWSJGXKmWfCqqtCdTVMn96hR+ztVdwMXpIkZUrv3nDttfDee/CHP7Q73N5exc+lRkmSMu2gg6C2Fl5+GTbYIO2wiooK6uvrW10vLy+nrq4uc/NTl3OpUZKkXLn4Ylh66aTB6ty5aYfZ26v4GbwkScq0AQPg8svhpZdg1Ki0w+ztVfwMXpIkZcO++8JOO8Fpp0GaN1j29ip+Bi9JkrIhBLjqquQYoaOPTnmckL29ip/F9ZIkZdPFF8NJJyXHCe25Z65nowyxuF6SpHxw3HEweHBynNDXX3foEXt7FQ+DlyRJ2dS9O4weDZ99BiNGtDvc3l7FxeAlSVK2VVYmb7yuuQaee67NoTU1NTQ0NLS41tDQQE1NTSZnqAyxxkuSpFz49ltYd11Yail45RXo0SPlsLKyMlL9uzqEQGNjY6ZnqUVkjZckSflkySXhT3+CiRPhoovSDrO3V3ExeEmSlCu77prsbDznHPjgg5RD7O1VXAxekiTl0uWXJ8uMRx1lb68SYI2XJEm5NmoUHHss3Hxz0uFeBc8aL0mS8tWRRyY7HY8/Hr76KtezUQYZvCRJyrVu3eDaa2HqVDj11A49YlPVwmTwkiQpH/zf//2vt9fzz7c51KaqhcsaL0mS8sW338IPfwgDBsD48Wl7e1VUVFBfX9/qenl5OXV1dRmepDrCGi9JkvLdkksmhfavv57sdkxj0qRJC3Vd+cPgJUlSPvn5z5P+XmeeCWmClE1VC5fBS5KkfBJC8tYLkhYTKdhUtXAZvCRJyjfl5XDGGXDPPXDffa1u21S1cFlcL0lSPpo1CwYPhu+/hzffhCWWyPWMtBAsrpckqZAsthhcfTXU1ydnOXaAvb3yn8FLkqR89dOfwkEHwcUXJ2+92mBvr8LgUqMkSfls6lRYay1Ybz14+umk+D4Fe3vlF5caJUkqRAMHwoUXwj//CWPHph1mb6/CYPCSJCnfHXwwbL45nHwyfPFFyiH29ioMBi9JkvJdWVlSaP/ll2kP0ba3V2EweEmSVAg22CA5RHv0aHjxxVa37e1VGCyulySpUHzzDay9Niy/fBK+unXL9YyUhsX1kiQVur594ZJL4JVX4JprOvSIvb3yi8FLkqRCss8+sO22UFMDn33W5lB7e+Ufg5ckSYUkBPjTn6ChIdnl2IaamhoaGhpaXGtoaKCmpiaTM1QbDF6SJBWatdZKQtff/gbPPJN2mL298o/BS5KkQlRTA+XlcOSRMHt2yiH29so/Bi9JkgpR795w+eXJGY6jRqUcYm+v/GPwkiSpUO22GwwdCmeeCR9/3Oq2vb3yj328JEkqZB98AOuuC3vuCe5WzBv28ZIkqRitthqccgrcdBM89VSHHrG3V+74xkuSpEI3fTqss05S9zVhAvTokXbovN5ezdtM9O7d2yXILuYbL0mSilWvXnDFFfDWW2kL7eext1duGbwkSSoGu+4KO++cttB+Hnt75ZbBS5KkYnH55UlPrzY62tvbK7cMXpIkFYvVVoPf/a7NQnt7e+WWwUuSpGJyyilQUQHHHANz5rS6bW+v3HJXoyRJxebuu2H33ZOlx2OPzfVsSpK7GiVJKhXDhsEOO8Dpp8Nnn+V6NmrG4CVJUrEJIXnbNX06jBjR4cdsrJp5Bi9JkorRWmvBCSfADTfA88+3O3xeY9X6+npijNTX11NdXW346mKdCl4hhL1DCG+GEBpDCJXNrleEEKaHECY0fa5pdm+jEMIbIYT3QwhXhBBCZ+YgSZLSOO00WGEFOPpomDu3zaE2Vs2Ozr7xmgjsATyT4t4HMcYNmz6HN7t+NVANrNH02bGTc5AkSan06QMXXwwvvwxjxrQ51Maq2dGp4BVjfDvG+G5Hx4cQlgf6xhifi8l2yr8CP+/MHCRJUhv22Qe22iqp9frii7TDbKyaHZms8VolhPBqCOHpEMKWTddWBCY3GzO56ZokScqEEJLzG7/8Es44I+0wG6tmR7vBK4TwWAhhYorPsDYe+wQYFGMcDJwA3BRC6AukqudK20gshFAdQhgfQhg/derU9qYqSZJSWX99OPJIuPpqeP31lENsrJodXdJANYTwFHBSjDFlh9N594EpwJMxxrWbru8HbB1jPKy9n2EDVUmSOuGLL2DNNWG99eDJJ5M3YcqYrDZQDSEMDCF0a/p6VZIi+g9jjJ8A34YQNm3azXggcE8m5iBJkprp3x/OPReefhr+/vcOP2Zvr67V2XYSu4cQJgObAQ+EEB5uuvVT4PUQwmvA7cDhMcZ5FX1HANcD7wMfAP/ozBwkSVIHHXIIDB4MJ54I33/f7nB7e3U9z2qUJKmUjBsHW2yR9Pg655w2h1ZUVFBfX9/qenl5OXV1dRmaYHHwrEZJkgQ/+QlUVcFFF8GHH7Y51N5eXc/gJUlSqbngAujePVlybIO9vbqewUuSpFKz4orJUuPdd8Mjj6QdZm+vrmfwkiSpFP3mN7DaanD88TB7dsoh9vbqehbXS5JUqu69F4YNg8svh2OPzfVsiorF9ZIkqaVdd4XttoMzzwRPiMkKg5ckSaUqBLjsMvj22zbPcVyQTVUXncFLkqRSts46cNRRMHo0vPZau8Ntqto51nhJklTqvvwS1lijQ+c42lS1Y6zxkiRJqS29NPzhD8k5jrff3uZQm6p2jsFLkiTBr38NG2wAJ50E06enHWZT1c4xeEmSJOjWLWkrMWkS/PGPaYfZVLVzDF6SJCmx9daw555w/vkwZUrKITZV7RyL6yVJ0v98+CH88Iewzz7w17/mejYFy+J6SZLUvlVXhRNOgL/9DV54IdezKToGL0mS1NKpp8JyyyXnOHZwZcymqh1j8JIkSS0tuSScey48/zzcdFO7w22q2nHWeEmSpNYaG2HjjeHTT+Hdd2GJJdIOtalqa9Z4SZKkjisrS85xnDIFLrqozaE2Ve04g5ckSUptiy3gF7+ACy9M+nulYVPVjjN4SZKk9C68MCmwHzEi7RCbqnacwUuSJKVXXg4nnpgU2adpL2FT1Y6zuF6SJLXtu+9gjTVglVVg3DgIIdczynsW10uSpEXTpw+MHAnPPQe33prr2RQ0g5ckSWrf8OGw4Ybw29/C9OkdesSmqq0ZvCRJUvu6dYNLL4WPPoJLLml3uE1VU7PGS5Ikddzuu8Ojj8J778Hyy6cdVupNVa3xkiRJnXfRRTBrFpx2WpvDbKqamsFLkiR13Oqrw7HHwg03wKuvph1mU9XUDF6SJGnhnHYa9O+f9PdKU7JkU9XUDF6SJGnh9OsHv/89PPkk3HdfyiE2VU3N4npJkrTwZs+G9ddP3nhNnAg9euR6RnnF4npJktR1evSAP/4R/v1vuOaaXM+mYBi8JEnSotl5Z9hmm2TZ8csvO/RIqTdVNXhJkqRFEwJcfDF88UVypFA7bKpqjZckSeqsgw+GG2+Et9+G1VZLO6yUmqpa4yVJkjLjD39Iar5+97s2h9lU1eAlSZI6a4UVksOzb78dnn027TCbqhq8JElSVzjppCSA2VS1TQYvSZLUeUsskSw5vvgi3HZbyiE2VbW4XpIkdZW5c2GjjeDrr+Gdd6Bnz1zPKGcsrpckSZnVrVvSVLWuDq68MtezyUsGL0mS1HWGDIGddkqWHadN69AjpdRU1eAlSZK61oUXwjffJOGrHaXWVNUaL0mS1PWqq+Evf4G33oLVV087rFibqlrjJUmSsufss2GxxWDEiDaHlVpTVYOXJEnqesstB6eckjRVHTcu7bBSa6pq8JIkSZlxwglJU9WTT7apahODlyRJyowllkiWHJ97Du64I+WQUmuqanG9JEnKnLlz4Uc/gpkz4c03k7qvEmBxvSRJyr5u3ZL2Eu+/D9dem+vZ5JzBS5IkZdZOO8E22yTLjl9/3aFHirWpqsFLkiRlVgjJW6/PP4cLLmh3eDE3Ve1U8Aoh7B1CeDOE0BhCqGx2vSqEMKHZpzGEsGHTvadCCO82u/eDTv49SJKkfLfRRlBVBZdeCpMntzm0pqaGhoaGFtcaGhqoqanJ5AyzorNvvCYCewDPNL8YY6yNMW4YY9wQOACoizFOaDakat79GON/OzkHSZJUCP7wB2hshNNPb3NYMTdV7VTwijG+HWN8t51h+wE3d+bnSJKkIlBRAcceC2PHwuuvpx1WzE1Vs1HjtQ+tg9cNTcuMp4cQQhbmIEmS8sGpp0K/fvDb36YdUsxNVdsNXiGEx0IIE1N8hnXg2U2AhhjjxGaXq2KM6wNbNn0OaOP56hDC+BDC+KlTp3bgb0eSJOW1pZeGmhp4+GF4/PGUQ4q5qWqXNFANITwFnBRjHL/A9UuBqTHGc9M8dxBQGWM8ur2fYQNVSZKKxIwZsNZasMwy8NJLUFZ8TRay3kA1hFAG7A3c0uxa9xDCMk1f9wB2ISnQlyRJpWLxxZNC+1degVtvzfVssqqz7SR2DyFMBjYDHgghPNzs9k+ByTHGD5td6wk8HEJ4HZgATAGu68wcJElSAaqqSo4SqqlJjhPqgGJoqupZjZIkKTcefhh23BEuuwyOO67NofOaqjbv79W7d++8rf1Kt9Ro8JIkSbkRI2y3HUyYAB98AEstlXZoRUUF9fX1ra6Xl5dTV1eXuTkuIg/JliRJ+SWE5AihadOSI4XaUCxNVQ1ekiQpdzbaCPbbLzlKaMqUtMOKpamqwUuSJOXWyJEwZw6cdVYbQ4qjqarBS5Ik5dYqq8CRR8KYMfDOOymHFEtTVYvrJUlS7k2dCqutBkOGwJ135no2nWZxvSRJyl8DB8LJJ8Ndd8Hzz+d6Nhlj8JIkSfnhN7+BZZdNDtAukBW5hWXwkiRJ+aFPHzjjDPjnP+HBBzv0SKF1s7fGS5Ik5Y/Zs2GddaBXL3j1VejWLe3QfO5mb42XJEnKfz16JAdov/EGtPP2qqampkXoAmhoaKCmpiaTM+wU33hJkqT80tgIG2+c7HR8911YfPGUw8rKykiVY0IINDY2ZnqWbfKNlyRJKgxlZXD++TBpElx1VdphhdjN3uAlSZLyz5AhyQHa554LX3+dckghdrM3eEmSpPx03nnJAdoXX5zydiF2s7fGS5Ik5a999oEHHoAPPkh6fBUIa7wkSVLhOeccmDEjOUi7CBi8JElS/lpzTTjkELjmGvjPfzr0SD43VTV4SZKk/HbGGUkj1TPPbHfovKaq9fX1xBipr6+nuro6b8KXwUuSJOW3FVeE446DG29MGqu2Id+bqhq8JElS/jvlFFhqKTj11DaHTZo0aaGuZ5vBS5Ik5b+ll07C1/33w7PPph2W701VDV6SJKkwHHssLL88jBgBadph5XtTVYOXJEkqDL17J4X2zz4L//hHyiH53lTVBqqSJKlwzJ4Na68NSy4Jr7ySnOuYh2ygKkmSCl+PHnD22fDaa3DbbbmezUIzeEmSpMKy336w/vpw+unJG7ACYvCSJEmFpawsOULo/ffhL3/p0CP50s3e4CVJkgrPLrvAZpvB738P06e3OTSfutkbvCRJUuEJAc49F6ZMgauuanNoPnWzN3hJkqTCtPXWsP32cN558M03aYflUzd7g5ckSSpcI0fCtGlwySVph+RTN3uDlyRJKlyVlbDnnnDxxTB1asoh+dTN3uAlSZIK2znnQEMDXHBBytv51M3ezvWSJKnwDR+eNFR9/31YccVcz8bO9ZIkqYiddRbMnQt/+EOuZ9Img5ckSSp8q6wChx4K118PH36Y69mkZfCSJEnF4bTToHv35O1XnjJ4SZKk4rDCCnD00XDjjfDWW7meTUoGL0mSVDxOOQX69IEzzsj1TFIyeEmSpOKxzDJwwglwxx3w8su5nk0rBi9JklRcTjgB+vdPar7yjMFLkiQVl759kyXHhx6CZ5/N9WxaMHhJkqTic/TRsOyyyVuvPGoWb/CSJEnFp3dvqKmBp5+Gxx/P9WzmM3hJkqTiVF0NK6+cV2+9DF6SJKk49ewJp58OL7wADzyQ69kABi9JklTMDjoIVl01CWCNjbmejcFLkiQVsR49kiOEJkyAO+/M9WwMXpIkqcj98pew9tpJN/u5c3M6FYOXJEkqbt26wdlnw9tvw80353QqBi9JklT89twTfvSjZNlx9uycTcPgJUmSil9ZGZxzTlLz9dFHuZtGZx4OIVwUQngnhPB6COGuEEK/ZvdGhBDeDyG8G0LYodn1jUIIbzTduyKEEDozB0mSpA7ZZReYODHZ5ZgjnX3j9SiwXoxxA+DfwAiAEMI6wL7AusCOwFUhhG5Nz1wNVANrNH127OQcJEmS2hdCUu+VQ50KXjHGR2KMc5r++DywUtPXw4BbYowzY4z/Ad4HNg4hLA/0jTE+F2OMwF+Bn3dmDpIkSYWiK2u8Dgb+0fT1ikDzBdTJTddWbPp6weuSJElFr3t7A0IIjwHLpbhVE2O8p2lMDTAHqJ33WIrxsY3r6X52NcmyJIMGDWpvqpIkSXmt3eAVYxzS1v0QwnBgF2DbpuVDSN5krdxs2ErAx03XV0pxPd3PHg2MBqisrMyP0y0lSZIWUWd3Ne4InALsFmNsaHbrXmDfEELPEMIqJEX0L8YYPwG+DSFs2rSb8UDgns7MQZIkqVC0+8arHVcCPYFHm7pCPB9jPDzG+GYI4TbgLZIlyKNijPN69B8B/AXoRVIT9o9W31WSJKkIdSp4xRhXb+PeSGBkiuvjgfU683MlSZIKkZ3rJUmSssTgJUmSlCUGL0mSpCwxeEmSJGWJwUuSJClLDF6SJElZYvCSJEnKEoOXJElSlhi8JEmSssTgJUmSlCUGL0mSpCwxeEmSJGWJwUuSJClLDF6SJElZYvCSJEnKkhBjzPUcOiSEMBWoz/CPWQb4PMM/QwvP30v+8XeSf/yd5Cd/L/knW7+T8hjjwAUvFkzwyoYQwvgYY2Wu56GW/L3kH38n+cffSX7y95J/cv07calRkiQpSwxekiRJWWLwaml0rieglPy95B9/J/nH30l+8veSf3L6O7HGS5IkKUt84yVJkpQlBq8mIYQdQwjvhhDeDyH8LtfzKXUhhJVDCE+GEN4OIbwZQjgu13NSIoTQLYTwagjh/lzPRYkQQr8Qwu0hhHea/jezWa7nVOpCCL9p+mfXxBDCzSGExXM9p1IUQhgTQvhvCGFis2v9QwiPhhDea/rr0tmck8GL5F8kwJ+AnYB1gP1CCOvkdlYlbw5wYozxh8CmwFH+TvLGccDbuZ6EWrgceCjGuDbwI/z95FQIYUXgWKAyxrge0A3YN7ezKll/AXZc4NrvgMdjjGsAjzf9OWsMXomNgfdjjB/GGGcBtwDDcjynkhZj/CTG+ErT19+S/ItkxdzOSiGElYCdgetzPRclQgh9gZ8CfwaIMc6KMX6V00kJoDvQK4TQHegNfJzj+ZSkGOMzwBcLXB4GjG36eizw82zOyeCVWBH4qNmfJ+O/5PNGCKECGAy8kOOpCC4Dfgs05nge+p9VganADU1LwNeHEJbI9aRKWYxxCvBHYBLwCfB1jPGR3M5KzSwbY/wEkv/IB36QzR9u8EqEFNfc7pkHQgh9gDuA42OM3+R6PqUshLAL8N8Y48u5nota6A78H3B1jHEw8D1ZXjpRS001Q8OAVYAVgCVCCPvndlbKFwavxGRg5WZ/XglfC+dcCKEHSeiqjTHemev5iJ8Au4UQ6kiW47cJIdyY2ymJ5J9fk2OM894I304SxJQ7Q4D/xBinxhhnA3cCm+d4Tvqfz0IIywM0/fW/2fzhBq/ES8AaIYRVQgiLkRRB3pvjOZW0EEIgqVl5O8Z4Sa7nI4gxjogxrhRjrCD538gTMUb/Kz7HYoyfAh+FENZqurQt8FYOp6RkiXHTEELvpn+WbYsbHvLJvcDwpq+HA/dk84d3z+YPy1cxxjkhhKOBh0l2n4yJMb6Z42mVup8ABwBvhBAmNF07Ncb4YO6mJOWtY4Dapv9w/BD4VY7nU9JijC+EEG4HXiHZof0qdrDPiRDCzcDWwDIhhMnAmcD5wG0hhENIQvLeWZ2TneslSZKyw6VGSZKkLDF4SZIkZYnBS5IkKUsMXpIkSVli8JIkScoSg5ckSVKWGLwkSZKyxOAlSZKUJf8P6jQRNxG/rLUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "plt.scatter(t,i,c = 'k')\n",
    "plt.plot(t, i_fitting,'r-')\n",
    "#plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47163e6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
